[core][imageregistration] Update h values

darfix/core/imageRegistration.py

@@ -26,7 +26,7 @@
 
 __authors__ = ["J. Garriga"]
 __license__ = "MIT"
-__date__ = "16/06/2021"
+__date__ = "17/06/2021"
 
 
 import enum
@@ -195,8 +195,9 @@ def improve_linear_shift(data, v, h, epsilon, steps, nimages=None, shift_approac
         iData = numpy.random.choice(iData, nimages, False)
 
     score = {}
-    utils.advancement_display(0, 2 * epsilon, "Finding shift")
-    for h_ in numpy.arange(h - epsilon, h + epsilon, 2 * epsilon / steps):
+    utils.advancement_display(0, h + epsilon, "Finding shift")
+    step = 2 * epsilon / steps
+    for h_ in numpy.arange(0, h + epsilon, step):
         result = numpy.zeros(data[0].shape)
         for iFrame in iData:
             shift = h_ * v * iFrame
@@ -205,12 +206,12 @@ def improve_linear_shift(data, v, h, epsilon, steps, nimages=None, shift_approac
         # Compute score using normalized variance
         # TODO: add more autofocus options
         score[h_] = normalized_variance(result)
-        utils.advancement_display(h_ - epsilon, 2 * epsilon, "Finding shift")
+        utils.advancement_display(h_ + step, h + epsilon, "Finding shift")
     optimal_h = max(score.keys(), key=(lambda k: score[k]))
     return optimal_h
 
 
-def shift_detection(data, h_max=0.5, steps=1000, shift_approach="linear"):
+def shift_detection(data, steps, shift_approach="linear"):
     """
     Finds the linear shift from a set of images.
 
@@ -233,7 +234,7 @@ def shift_detection(data, h_max=0.5, steps=1000, shift_approach="linear"):
     v = normalize(shift)
     v_ = 2 * shift / len(data)
     h = numpy.sqrt(v_[0]**2 + v_[1]**2)
-    epsilon = 0.5 * h
+    epsilon = 2 * h
     h = improve_linear_shift(data, v, h, epsilon, steps, shift_approach=shift_approach)
     return numpy.outer(h * v, numpy.arange(len(data)))